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Evidence of Chaos in EEG Signals: An Application to BCI

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Advances in Chaos Theory and Intelligent Control

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 337))

Abstract

The recent science and technology studies in neuroscience and machine learning have focused attention on investigating the functioning of the brain through nonlinear analysis. The brain is a nonlinear dynamic system, imparting randomness and nonlinearity in the EEG signals. The stochastic nature of the brain seeks the paramount importance of understanding the underlying neurophysiology. The nonlinear analysis of the dynamic structure may help to reveal the complex behavior of the brain signals. EEG signal analysis is helpful in various clinical applications to characterize the normal and diseased brain states. The EEG is used in predicting epileptic seizures, classifying the sleep stages, measuring the depth of anesthesia, and detecting the abnormal brain states. With the onset of EEG-based brain-computer interfaces, the characteristics of brain signals are used to control the devices through different mental states. Hence, the need to understand the brain state is important and crucial. In this chapter, the author introduces the theory and methods of chaos theory measurements and its applications in EEG signal analysis. A broad perspective of the techniques and implementation of the Correlation Dimension, Lyapunov Exponents, Fractal Dimension, Approximate Entropy, Sample Entropy, Hurst Exponent, Lempel-Ziv complexity, Hopf Bifurcation Theorem and Higher-order spectra is explained and their usage in EEG signal analysis is mentioned. We suggest that chaos theory provides not only potentially valuable diagnostic information but also a deeper understanding of neuropathological mechanisms underlying the brain in ways that are not possible by conventional linear analysis.

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Author information

Authors and Affiliations

  1. Department of Computer Science & Engineering, Medical Imaging Research Centre, Dayananda Sagar College of Engineering, Bangalore, 560078, India

    Kusuma Mohanchandra

  2. Department of Computer Science & Engineering, PESIT South Campus, Bangalore, 560100, India

    Snehanshu Saha & K. Srikanta Murthy

  3. The Center for Basic Initiatives in Mathematical Modeling and Computation (CBIMMC), PESIT South Campus, Bangalore, 560100, India

    Snehanshu Saha

Authors
  1. Kusuma Mohanchandra
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  2. Snehanshu Saha
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  3. K. Srikanta Murthy
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Corresponding author

Correspondence to Kusuma Mohanchandra .

Editor information

Editors and Affiliations

  1. Benha University, Benha, Egypt

    Ahmad Taher Azar

  2. Chennai, India

    Sundarapandian Vaidyanathan

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Mohanchandra, K., Saha, S., Murthy, K.S. (2016). Evidence of Chaos in EEG Signals: An Application to BCI. In: Azar, A., Vaidyanathan, S. (eds) Advances in Chaos Theory and Intelligent Control. Studies in Fuzziness and Soft Computing, vol 337. Springer, Cham. https://doi.org/10.1007/978-3-319-30340-6_25

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  • DOI: https://doi.org/10.1007/978-3-319-30340-6_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-30338-3

  • Online ISBN: 978-3-319-30340-6

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